






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						):
          super().__init__()
          UpperCAmelCase__      :  List[Any]				      =			value_function
          UpperCAmelCase__      :  Any				      =			unet
          UpperCAmelCase__      :  Tuple				      =			scheduler
          UpperCAmelCase__      :  List[Any]				      =			env
          UpperCAmelCase__      :  List[Any]				      =			env.get_dataset()
          UpperCAmelCase__      :  List[Any]				      =			{}
          for key in self.data.keys():
               try:
                    UpperCAmelCase__      :  Tuple				      =			self.data[key].mean()
               except:  # noqa: E722
                    pass
          UpperCAmelCase__      :  str				      =			{}
          for key in self.data.keys():
               try:
                    UpperCAmelCase__      :  Dict				      =			self.data[key].std()
               except:  # noqa: E722
                    pass
          UpperCAmelCase__      :  Union[str, Any]				      =			env.observation_space.shape[0]
          UpperCAmelCase__      :  Any				      =			env.action_space.shape[0]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return (x_in - self.means[key]) / self.stds[key]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return x_in * self.stds[key] + self.means[key]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if type(_lowerCAmelCase		) is dict:
               return {k: self.to_torch(_lowerCAmelCase		) for k, v in x_in.items()}
          elif torch.is_tensor(_lowerCAmelCase		):
               return x_in.to(self.unet.device		)
          return torch.tensor(_lowerCAmelCase	,						device=self.unet.device		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          for key, val in cond.items():
               UpperCAmelCase__      :  List[Any]				      =			val.clone()
          return x_in




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			x.shape[0]
          UpperCAmelCase__      :  List[str]				      =			None
          for i in tqdm.tqdm(self.scheduler.timesteps		):
               # create batch of timesteps to pass into model
               UpperCAmelCase__      :  Dict				      =			torch.full((batch_size,)	,						_lowerCAmelCase	,						device=self.unet.device	,						dtype=torch.long		)
               for _ in range(_lowerCAmelCase		):
                    with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         UpperCAmelCase__      :  Tuple				      =			self.value_function(x.permute(0	,						2	,						1		)	,						_lowerCAmelCase		).sample
                         UpperCAmelCase__      :  List[Any]				      =			torch.autograd.grad([y.sum()]	,						[x]		)[0]

                         UpperCAmelCase__      :  Union[str, Any]				      =			self.scheduler._get_variance(_lowerCAmelCase		)
                         UpperCAmelCase__      :  Optional[int]				      =			torch.exp(0.5 * posterior_variance		)
                         UpperCAmelCase__      :  str				      =			model_std * grad

                    UpperCAmelCase__      :  Optional[int]				      =			0
                    UpperCAmelCase__      :  Any				      =			x.detach()
                    UpperCAmelCase__      :  List[Any]				      =			x + scale * grad
                    UpperCAmelCase__      :  str				      =			self.reset_xa(_lowerCAmelCase	,						_lowerCAmelCase	,						self.action_dim		)

               UpperCAmelCase__      :  Tuple				      =			self.unet(x.permute(0	,						2	,						1		)	,						_lowerCAmelCase		).sample.permute(0	,						2	,						1		)

               # TODO: verify deprecation of this kwarg
               UpperCAmelCase__      :  Tuple				      =			self.scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						predict_epsilon=_lowerCAmelCase		)["""prev_sample"""]

               # apply conditions to the trajectory (set the initial state)
               UpperCAmelCase__      :  Any				      =			self.reset_xa(_lowerCAmelCase	,						_lowerCAmelCase	,						self.action_dim		)
               UpperCAmelCase__      :  List[str]				      =			self.to_torch(_lowerCAmelCase		)
          return x, y




     def __call__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=64	,						_lowerCAmelCase=32	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.1		):
          # normalize the observations and create  batch dimension
          UpperCAmelCase__      :  str				      =			self.normalize(_lowerCAmelCase	,						"""observations"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			obs[None].repeat(_lowerCAmelCase	,						axis=0		)

          UpperCAmelCase__      :  List[Any]				      =			{0: self.to_torch(_lowerCAmelCase		)}
          UpperCAmelCase__      :  Union[str, Any]				      =			(batch_size, planning_horizon, self.state_dim + self.action_dim)

          # generate initial noise and apply our conditions (to make the trajectories start at current state)
          UpperCAmelCase__      :  Dict				      =			randn_tensor(_lowerCAmelCase	,						device=self.unet.device		)
          UpperCAmelCase__      :  int				      =			self.reset_xa(_lowerCAmelCase	,						_lowerCAmelCase	,						self.action_dim		)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.to_torch(_lowerCAmelCase		)

          # run the diffusion process
          UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			self.run_diffusion(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)

          # sort output trajectories by value
          UpperCAmelCase__      :  Optional[int]				      =			y.argsort(0	,						descending=_lowerCAmelCase		).squeeze()
          UpperCAmelCase__      :  List[str]				      =			x[sorted_idx]
          UpperCAmelCase__      :  Tuple				      =			sorted_values[:, :, : self.action_dim]
          UpperCAmelCase__      :  List[Any]				      =			actions.detach().cpu().numpy()
          UpperCAmelCase__      :  List[str]				      =			self.de_normalize(_lowerCAmelCase	,						key="""actions"""		)

          # select the action with the highest value
          if y is not None:
               UpperCAmelCase__      :  int				      =			0
          else:
               # if we didn't run value guiding, select a random action
               UpperCAmelCase__      :  Optional[Any]				      =			np.random.randint(0	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  Any				      =			denorm_actions[selected_index, 0]
          return denorm_actions













"""simple docstring"""





import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

SCREAMING_SNAKE_CASE__   =  [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]

class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase = None					)						->	Dict:
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			os.path.abspath(os.path.join("""examples""" ,		"""by_feature"""					)					)
            lowerCAmelCase    		=			os.path.abspath("""examples"""					)
            for item in os.listdir(lowercase					):
                  if item not in EXCLUDE_EXAMPLES:
                        lowerCAmelCase    		=			os.path.join(lowercase ,		lowercase					)
                        if os.path.isfile(lowercase					) and ".py" in item_path:
                              with self.subTest(
                                  tested_script=lowercase ,		feature_script=lowercase ,		tested_section="""main()""" if parser_only else """training_function()""" ,		):
                                    lowerCAmelCase    		=			compare_against_test(
                                        os.path.join(lowercase ,		lowercase					) ,		lowercase ,		lowercase ,		lowercase					)
                                    lowerCAmelCase    		=			"""\n""".join(lowercase					)
                                    if special_strings is not None:
                                          for string in special_strings:
                                                lowerCAmelCase    		=			diff.replace(lowercase ,		""""""					)
                                    self.assertEqual(lowercase ,		""""""					)





      def        _snake_case     (			self					)						->	List[Any]:
            self.one_complete_example("""complete_nlp_example.py""" ,		lowercase					)
            self.one_complete_example("""complete_nlp_example.py""" ,		lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			os.path.abspath(os.path.join("""examples""" ,		"""cv_example.py"""					)					)
            lowerCAmelCase    		=			[
                """ """ * 16 + """{\n\n""",
                """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
                """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
                """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
                """ """ * 20 + """\"epoch\": epoch,\n\n""",
                """ """ * 16 + """},\n\n""",
                """ """ * 16 + """step=epoch,\n""",
                """ """ * 12,
                """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
            ]
            self.one_complete_example("""complete_cv_example.py""" ,		lowercase ,		lowercase ,		lowercase					)
            self.one_complete_example("""complete_cv_example.py""" ,		lowercase ,		lowercase ,		lowercase					)







@mock.patch.dict(os.environ	,    {'TESTING_MOCKED_DATALOADERS': '1'}   )
class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = False





      @classmethod
      def        _snake_case     (			cls					)						->	Optional[int]:
            super().setUpClass()
            lowerCAmelCase    		=			tempfile.mkdtemp()
            lowerCAmelCase    		=			os.path.join(cls._tmpdir ,		"""default_config.yml"""					)

            write_basic_config(save_location=cls.configPath					)
            lowerCAmelCase    		=			["""accelerate""", """launch""", """--config_file""", cls.configPath]





      @classmethod
      def        _snake_case     (			cls					)						->	Optional[int]:
            super().tearDownClass()
            shutil.rmtree(cls._tmpdir					)





      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
            run_command(self._launch_args + testargs					)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,		"""epoch_0"""					)					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs					)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,		"""step_2"""					)					)					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir ,		"epoch_0"					)}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
            self.assertNotIn("""epoch 0:""" ,		lowercase					)
            self.assertIn("""epoch 1:""" ,		lowercase					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir ,		"step_2"					)}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
            if torch.cuda.is_available():
                  lowerCAmelCase    		=			torch.cuda.device_count()
            else:
                  lowerCAmelCase    		=			1
            if num_processes > 1:
                  self.assertNotIn("""epoch 0:""" ,		lowercase					)
                  self.assertIn("""epoch 1:""" ,		lowercase					)
            else:
                  self.assertIn("""epoch 0:""" ,		lowercase					)
                  self.assertIn("""epoch 1:""" ,		lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
            with mock.patch.dict(os.environ ,		{"""TESTING_MOCKED_DATALOADERS""": """0"""}					):
                  lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
                  lowerCAmelCase    		=			re.findall("""({.+})""" ,		lowercase					)
                  lowerCAmelCase    		=			[r for r in results if """accuracy""" in r][-1]
                  lowerCAmelCase    		=			ast.literal_eval(lowercase					)
                  self.assertGreaterEqual(results["""accuracy"""] ,		0.75					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			["""examples/by_feature/multi_process_metrics.py"""]
            run_command(self._launch_args + testargs					)





      @require_trackers
      @mock.patch.dict(os.environ ,		{"""WANDB_MODE""": """offline"""}					)
      def        _snake_case     (			self					)						->	Any:
            with tempfile.TemporaryDirectory() as tmpdir:
                  lowerCAmelCase    		=			f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
                  run_command(self._launch_args + testargs					)
                  self.assertTrue(os.path.exists(os.path.join(lowercase ,		"""tracking"""					)					)					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			["""examples/by_feature/gradient_accumulation.py"""]
            run_command(self._launch_args + testargs					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			["""examples/by_feature/local_sgd.py"""]
            run_command(self._launch_args + testargs					)






